Recent Publications

Publications The latest 10 papers published or under review

Extracting Spatial Information about Events from Text

Jin-Woo Chung
PhD Dissertation, KAIST, 2018

Detection of Non-Standard Meaning Usage with Word Embedding

Huije Lee, Hancheol Park, Wonsuk Yang, and Jong C. Park
Human-Computer Interaction Korea (HCI), Jeongseon, Korea, January 31-February 2, 2018.
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蹂 뿰援ъ뿉꽌뒗 遺꾩궛 몴긽 湲곕쾿쑝濡 뀓뒪듃뿉꽌 궗쟾긽쓽 쓽誘몃줈 궗슜릺吏 븡 뼱쐶(씠븯, 鍮꾪몴以 쓽誘 뼱쐶)瑜 깘吏븯뒗 紐⑤뜽쓣 젣븞븳떎. 뼱쐶쓽 뼱삎 룞씪븯굹 鍮꾪몴以 쓽誘몃줈 궗슜릺뒗 寃쎌슦瑜 뙋떒븯뒗 寃껋 옄룞솕맂 뀓뒪듃 遺꾩꽍 諛 삤뿭쓽 臾몄젣瑜 빐寃고븯뒗 뜲 以묒슂븳 슂냼씠떎. 蹂 뿰援ъ뿉꽌뒗 遺꾩궛 몴긽 湲곕쾿쑝濡 깮꽦맂 臾몃㎘ 諛 긽 떒뼱 踰≫꽣瑜 씠슜븯뿬, 긽 떒뼱媛 二쇱뼱吏 臾몃㎘ 궡뿉꽌 쟻빀븳吏瑜 寃利앺븯怨 긽 떒뼱媛 鍮꾪몴以 쓽誘몃줈 궗슜릺뿀뒗吏 뿬遺瑜 뙋떒븳떎. 蹂 뿰援ъ뿉꽌뒗 湲곗〈 뿰援ъ뿉꽌쓽 臾몃㎘ 踰≫꽣 깮꽦 諛⑹떇씠 吏땲뒗 臾몄젣젏쓣 빐寃고븯湲 쐞빐, 넻빀쟻씤 臾몃㎘ 젙蹂대 몴긽븯뒗 諛⑸쾿怨 臾몃㎘ 궡 떒뼱뱾쓽 媛以묒튂瑜 二쇰뒗 諛⑸쾿쓣 젣븞븳떎. 젣븞븯뒗 諛⑸쾿 듃쐞꽣 뜲씠꽣瑜 씠슜븳 떎뿕뿉꽌 湲곗〈뿉 젣븞맂 紐⑤뜽蹂대떎 뜑 넂 꽦뒫쓣 蹂댁떎.

Predicting Symptoms of Depression for Social Media Users via Linguistic Patterns

Hoyun Song, Hancheol Park, Wonsuk Yang, and Jong C. Park
Korea Software Congress (KSC), Busan, Korea, December 20-22, 2017.
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슦슱利앹 媛쒖씤쓽 씪긽 湲곕뒫 븯 諛 떎뼇븳 궗쉶쟻 臾몄젣瑜 빞湲고븷 닔 엳湲 븣臾몄뿉 議곌린 吏꾨떒씠 以묒슂븯떎. 씠윭븳 議곌린 吏꾨떒쓽 떆룄濡쒖꽌, 蹂 뿰援щ뒗 냼뀥 誘몃뵒뼱 뀓뒪듃瑜 씠슜븯뿬 궗슜옄뱾쓽 슦슱利 뿬遺瑜 삁痢≫븯뒗 紐⑤뜽쓣 젣븞븳떎. 蹂 뿰援ъ뿉꽌뒗 鍮꾩젙삎 뀓뒪듃씤 냼뀥 誘몃뵒뼱 뀓뒪듃 긽뿉꽌 湲곗〈쓽 뼱쐶 湲곕컲 紐⑤뜽씠 吏땶 븳怨꾩젏씤 뼱쐶 留ㅼ묶 臾몄젣 諛 슦슱利앹쓣 寃り퀬 엳吏 븡 궗슜옄뱾쓽 슦슱利 愿젴 뼱쐶 궗슜怨 愿젴븳 臾몄젣젏쓣 빐寃고븯湲 쐞빐, 蹂대떎 떖痢듭쟻씤 뼵뼱븰쟻 뙣꽩쓣 씠슜븳 紐⑤뜽쓣 젣떆븳떎. 蹂 뿰援ъ쓽 떎뿕쓣 넻빐 궗슜옄쓽 슦슱利 뿬遺瑜 삁痢≫븿뿉 엳뼱 뼵뼱븰쟻 뙣꽩쓣 븿猿 쟻슜븷 寃쎌슦 떒닚븳 뼱쐶 湲곕컲 紐⑤뜽뿉 鍮꾪빐 뜑슧 슚怨쇱쟻엫쓣 솗씤븷 닔 엳뿀떎.

Extraction of Gene-Environment Interaction from the Biomedical Literature

Jinseon You, Jin-Woo Chung, Wonsuk Yang, and Jong C. Park
Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), pp. 865874, Taipei, Taiwan, November 27밆ecember 1, 2017.
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Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.

Inferring Implicit Event Locations from Context with Distributional Similarities

Jin-Woo Chung, Wonsuk Yang, Jinseon You, and Jong C. Park
Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 979-985, Melbourne, Australia, August 19-25, 2017.
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Automatic event location extraction from text plays a crucial role in many applications such as infectious disease surveillance and natural disaster monitoring. The fundamental limitation of previous work such as SpaceEval is the limited scope of extraction, targeting only at locations that are explicitly stated in a syntactic structure. This leads to missing a lot of implicit information inferable from context in a document, which amounts to nearly 40% of the entire location information. To overcome this limitation for the first time, we present a system that infers the implicit event locations from a given document. Our system exploits distributional semantics, based on the hypothesis that if two events are described by similar expressions, it is likely that they occur in the same location. For example, if 쏛 bomb exploded causing 30 victims and 쐌any people died from terrorist attack in Boston are reported in the same document, it is highly likely that the bomb exploded in Boston. Our system shows good performance of a 0.58 F1-score, where state-of-the-art classifiers for intra-sentential spatiotemporal relations achieve around 0.60 F1-scores.

Using syntactic structure to extract prominent gene regulatory network from the literature

Wonsuk Yang
MS Thesis, KAIST, 2017.

Neural Theorem Prover with Word Embedding for Efficient Automatic Annotation

Wonsuk Yang, Hancheol Park, and Jong C. Park
Journal of KIISE, Vol. 44, No. 4, pp. 399-410, April, 2017.

Addressing low-resource problems in statistical machine translation of manual signals in sign language

Hancheol Park, Jung-Ho Kim, and Jong C. Park
Journal of KIISE, Vol. 44, No. 2, pp. 163-170, February, 2017.

Neural Theorem Prover with Word Embedding for Efficient Automatic Annotation

Wonsuk Yang, Hancheol Park, and Jong C. Park
Proceedings of the 28th Annual Conference on Human and Cognitive Language Technology (HCLT) pp. 79-84, Busan, Korea, October 07-08, 2016.
(selected as best paper)
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蹂 뿰援щ뒗 쟾臾멸린愿뿉꽌 깮궛릺뒗 寃利앸맂 臾몄꽌瑜 쎒긽쓽 닔留롮 寃利앸릺吏 븡 臾몄꽌뿉 옄룞 二쇱꽍븯뿬 떊 猶곕룄 뼢긽 諛 떖솕 젙蹂대 옄룞쑝濡 異붽븯뒗 떆뒪뀥쓣 꽕怨꾪븯뒗 寃껋쓣 紐⑺몴濡 븳떎. 씠瑜 쐞빐 솢슜 媛뒫 븳 떆뒪뀥씤 씤怨 떊寃 젙由 利앸챸怨(neural theorem prover)媛 洹쒕え 留먮춬移섏뿉 쟻슜릺吏 븡뒗떎뒗 洹쇰낯 쟻씤 臾몄젣瑜 빐寃고븯湲 쐞빐 궡遺 닚솚 紐⑤뱢쓣 떒뼱 엫踰좊뵫 紐⑤뱢濡 援먯껜븯뿬 옱援ъ텞 븯떎. 븰뒿 떆媛꾩쓽 쉷湲곗쟻씤 媛먯냼瑜 엯利앺븯湲 쐞빐 援媛븫젙蹂댁꽱꽣쓽 븫 삁諛 諛 떎泥쒖뿉 븳 寃利앸맂 臾몄꽌뱾뿉꽌 異붿텧븳 28,844媛 紐낆젣瑜 쐞궎뵾뵒븘 븫 愿젴 臾몄꽌뿉꽌 異붿텧븳 7,844媛 紐낆젣뿉 二쇱꽍븯뒗 궗濡瑜 넻븯뿬 湲곗〈쓽 떆뒪뀥怨 옱援ъ텞븳 떆뒪뀥쓣 蹂묐젹 鍮꾧탳븯떎. 룞씪븳 솚寃쎌뿉꽌 湲곗〈 떆뒪뀥쓽 븰뒿 떆媛꾩씠 553.8씪濡 異 젙맂 寃껋뿉 鍮꾪빐 옱援ъ텞븳 떆뒪뀥 93.1遺 궡濡 븰뒿씠 셿猷뚮릺뿀떎. 蹂 뿰援ъ쓽 옣젏 씤怨 떊寃 젙由 利 紐낃퀎媛 紐⑤뱢솕 媛뒫븳 鍮꾩꽑삎 떆뒪뀥씠湲곗뿉 떎瑜 꽑삎 끉由 諛 옄뿰뼵뼱 泥섎━ 紐⑤뱢뱾怨 蹂묐젹쟻쑝濡 寃고빀 맆 닔 엳쓬뿉룄 쁽떎 궗濡뿉 씠瑜 쟻슜 遺덇뒫븯寃 뻽뜕 븰뒿 떆媛꾩뿉 븳 臾몄젣瑜 빐냼뻽떎뒗 젏씠떎.

Enhanced sign language transcription system via hand tracking and pose estimation

Jung-Ho Kim, Najoung Kim, Hancheol Park, and Jong C. Park
Journal of Computing Science and Engineering, Vol. 10, No. 3, pp. 95-101, September, 2016.
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In this study, we propose a new system for constructing parallel corpora for sign languages, which are generally underresourced in comparison to spoken languages. In order to achieve scalability and accessibility regarding data collection and corpus construction, our system utilizes deep learning-based techniques and predicts depth information to perform pose estimation on hand information obtainable from video recordings by a single RGB camera. These estimated poses are then transcribed into expressions in SignWriting. We evaluate the accuracy of hand tracking and hand pose estimation modules of our system quantitatively, using the American Sign Language Image Dataset and the American Sign Language Lexicon Video Dataset. The evaluation results show that our transcription system has a high potential to be successfully employed in constructing a sizable sign language corpus using various types of video resources.